Use default head dropout prob if not provided by model

src/adapters/heads/base.py

@@ -65,15 +65,23 @@ def __init__(self, name):
         self.config = {}
         self.name = name
 
-    def build(self, model):
-        model_config = model.config
-        pred_head = []
+    def _get_dropout_prob(self, model_config):
+        # try to infer dropout prob from various sources, default to 0.0
         if "dropout_prob" in self.config and self.config["dropout_prob"] is not None:
             dropout_prob = self.config["dropout_prob"]
         elif hasattr(model_config, "classifier_dropout") and model_config.classifier_dropout is not None:
             dropout_prob = model_config.classifier_dropout
-        else:
+        elif hasattr(model_config, "hidden_dropout_prob") and model_config.hidden_dropout_prob is not None:
             dropout_prob = model_config.hidden_dropout_prob
+        else:
+            dropout_prob = 0.0
+
+        return dropout_prob
+
+    def build(self, model):
+        model_config = model.config
+        pred_head = []
+        dropout_prob = self._get_dropout_prob(model_config)
         bias = self.config.get("bias", True)
         for l_id in range(self.config["layers"]):
             pred_head.append(nn.Dropout(dropout_prob))

src/adapters/heads/dependency_parsing.py

@@ -81,7 +81,7 @@ def build(self, model):
             n_in=model.config.hidden_size, n_out=self.config["num_labels"], bias_x=True, bias_y=True
         )
 
-        self.dropout = nn.Dropout(model.config.hidden_dropout_prob)
+        self.dropout = nn.Dropout(self._get_dropout_prob(model.config))
 
         self.loss_fn = CrossEntropyLoss()
 

tests/test_llama.py

@@ -29,7 +29,6 @@ class LlamaAdapterTestBase(AdapterTestBase):
         num_attention_heads=4,
         intermediate_size=37,
         hidden_act="gelu",
-        hidden_dropout_prob=0.1,
         pad_token_id=0,
     )
     tokenizer_name = "openlm-research/open_llama_13b"